Microsoft SQL Server – 25 years old and still going strong

For more than two decades now, several enterprises have managed all facets of their relational data by the help of SQL Server. With R and Python model training and scoring, SQL Server is now moving further than querying relational data by unifying graph and relational data and placing Machine Learning to where the data is. It is easy to integrate and analyze data across different types of data – also when the amount and types of data increases.

SQL Server 2019 has created a unified data platform with Apache SparkTM and Hadoop Distributed File System (HDFS) together with DQL Server as a single, integrated solution.

The major news in SQL Server 2019 is the Big Data clusters, but don’t forget the many improvements that continue to improve and transform the centerpiece of the Microsoft data platform stack.

SQL Server is available in a local version as well as in Microsoft Azure.

What’s new in Microsoft SQL Server 2019?

Calling all analysts, data scientists, database administrators and developers! New features in Microsoft SQL Server 2019 makes it possible for you to be even more productive:

Big data clusters deploy SQL Server side by side with Spark and HDFS into a single combined cluster for big scale and analytics.

Data virtualization connectors to Oracle, MongoDB, Teradata, other SQL Servers, and other ODBC compliance data sources

SQL Server on Linux gets more features like agent replication, distributed transactions, PolyBase, and Machine Learning services.

Use Java code in your SQL queries in addition to already supported languages like .NET, Python, and R.

More support for Kubernetes and containers like Always On Availability Groups on Kubernetes.

Graph SQL query improvements like transitive closure.

The centre of SQL Server is performance, security and availability!

Tools and services

In addition to storing, processing and securing data in the database engine, Microsoft SQL includes a range of tools and services:

Integration Services

Offers a platform where you can build high performance data integration solutions, including packages that provide extract, transform, and load (ETL) processing for data warehousing.

Machine Learning Services

Enables Python and R to run relational data. Open-source packages and frameworks can be used, as well as Microsoft Python and R packages for predictive analytics and Machine Learning.

Big Data Clusters

Offers three main functionalities: data virtualization, managed SQL Server, Spark and data lake (store high volume data in a data lake and access it easily using either SQL or Spark), complete AI platform.

Analysis Services

An analytics engine used to process business data for Business Intelligence and data visualization. Supports tabular models, multidimensional models, and Power Pivot for SharePoint.

SQL Graph

Graph database functions for SQL Server that simplify the process of modeling data containing complex many-to-many and hierarchical relationships.

Reporting Services

SQL SSRS provides a range of tools and services that can create, implement, and manage mobile and paginated reports. The reports can be viewed in a browser, on a mobile device, or in an email.

Master Data Services

Lets you manage a master set of your company’s data. You can organize data into models, set up rules for updating data and control who updates data.

Data Quality Services

A knowledge-based data quality product that enables you to build a knowledge base and use it to perform data quality tasks e.g. corrections, enrichment, standardization, etc.

Replication

Set of technologies for copying and distributing data and database objects from one database to another, and then synchronizing between databases for consistency.